Semantic Segmentation using Fully Convolutional Networks over the years
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https://meetshah1995.github.io/semantic-segmentation/deep-learning/pytorch/visdom/2017/06/01/semantic-segmentation-over-the-years.html
Semantic Segmentation
Introduction
Semantic Segmentation of an image is to assign each pixel in the input image a semantic class in order to get a pixel-wise dense classification. While semantic segmentation / scene parsing has been a part of the computer vision community since 2007, but much like other areas in computer vision, major breakthrough came when fully convolutional neural networks were first used by 2014 Long et. al. to perform end-to-end segmentation of natural images.
Figure : Example of semantic segmentation (Left) generated by FCN-8s ( trained using pytorch-semseg repository) overlayed on the input image (Right)
The FCN-8s architecture put forth achieved a 20% relative improvement to 62.2% mean IU on Pascal VOC 2012 dataset. This architecture was in my opinion a baseline for semantic segmentation on top of which several newer and better architectures were developed.
Fully Convolutional Networks (FCNs) are being used for semantic segmentation of natural images, for multi-modal medical image analysis and multispectral satellite image segmentation. Very similar to deep classification networks like AlexNet, VGG, ResNet etc. there is also a large variety of deep architectures that perform semantic segmentation.
I summarize networks like FCN, SegNet, U-Net, FC-Densenet E-Net & Link-Net, RefineNet, PSPNet, Mask-RCNN, and some semi-supervised approaches like DecoupledNet and GAN-SS here and provide reference PyTorch and Keras (in progress) implementations for a number of them. In the last part of the post I summarize some popular datasets and visualize a few results with the trained networks.
更多内容请查看原文。- Semantic Segmentation using Fully Convolutional Networks over the years
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